论文标题
EZFF：用于多目标参数化和分子动力学间力场的不确定性定量的Python库
EZFF: Python Library for Multi-Objective Parameterization and Uncertainty Quantification of Interatomic Forcefields for Molecular Dynamics
论文作者
论文摘要
原子间力场的参数化是执行分子动力学模拟的必要第一步。这是一个非平凡的全局优化问题，涉及对一个或多个属性的多个经验变量进行量化。我们提出了EZFF，这是一个轻巧的Python库，用于使用基于遗传的全局优化方法在几种分子动力学引擎中针对多个目标实现的几种类型的原子质力场参数化。 EZFF方案提供了独特的功能，例如由多力场相互作用组成的混合力场的参数化以及力场参数中不确定性的内置量化，并且可以很容易地扩展到其他力场功能形式以及MD引擎。
Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.
